# Twin Modeling of Individual Symptoms of Depression and Generalized Anxiety: A Symptom Network Approach

> **NIH NIH F31** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2020 · $40,358

## Abstract

Project Summary/Abstract
 The goals of this fellowship are to further develop the applicant's knowledge and skills in advanced
computational methods (i.e., twin modeling and network modeling), the comorbidity of Major Depressive
Disorder (MDD) and Generalized Anxiety Disorder (GAD), and basic neuroscience and genetics. In line with
these goals, a cornerstone of the applicant's training will be the statistical and theoretical training he receives
through workshops, coursework, annual visits to Virginia Commonwealth University (VCU) to meet with Drs.
Gillespie and Neale, regular sponsor and co-sponsor meetings, and professional development activities. The
project will serve as the applicant's dissertation and help him pursue his goal of becoming an independent
investigator who uses advanced computational analyses of large datasets and multi-method laboratory studies
to study the etiology, maintenance, and recurrence of phenotypes of depression and anxiety. In addition to the
skills to be gained by the applicant, the project's goals will greatly advance the understanding of MDD and
GAD symptom etiology by testing the central tenet of a novel theory of psychopathology (network theory).
Understanding the causes of symptoms and their covariance is critical, as different causal models have
markedly different implications for intervention. Additionally, the study is consistent with the NIMH's strategic
objective to define mechanisms of complex behaviors and NIH's increased emphasis on replicability.
 Several twin studies have estimated the genetic and environmental etiology of individual MDD
symptoms. However, no studies have considered the possibility of causal relationships between symptoms as
hypothesized by the network theory. The present study will therefore use a novel method - direction of
causation (DoC) modeling - of twin data to (1) test putative causal relationships between individual MDD and
GAD symptoms and estimate the contributions of genetics and environment to each symptom, (2) evaluate the
replicability of the best fitting model in aim 1 in an independent twin sample, and (3) explore sex differences in
the phenotypic causal pathways and genetic and environmental liabilities of each symptom. This project greatly
extends prior studies of individual MDD symptoms by testing putatively causal pathways hypothesized by the
network theory and including both MDD and GAD symptoms in the same model, which is important given their
high comorbidity and potential causal relationships between symptoms of the two disorders. Mentorship for this
project will be provided by experts in the areas of twin and DoC modeling, network theory and modeling, the
comorbidity of depressive and anxiety disorders, and neuroscience (sponsors: Shankman, Gillespie, and Fried;
OSCs: Neale, Roitman). This fellowship will not only be an important step in the applicant's research career,
but the proposed study's primary objective of testing different causal models of symptom ...

## Key facts

- **NIH application ID:** 10065869
- **Project number:** 1F31MH123042-01A1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Carter Funkhouser
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $40,358
- **Award type:** 1
- **Project period:** 2020-08-16 → 2022-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10065869

## Citation

> US National Institutes of Health, RePORTER application 10065869, Twin Modeling of Individual Symptoms of Depression and Generalized Anxiety: A Symptom Network Approach (1F31MH123042-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10065869. Licensed CC0.

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